InnoFrank/Lagos-Abuja-Rent-Prediction

Machine Learning project for predicting house rent prices in Lagos and Abuja, Nigeria using property features and a Random Forest Regressor. # Lagos/Abuja Rent Prediction 🏠 A Machine Learning project for predicting house rent prices in Lagos and Abuja, Nigeria. ## 📌 Project Overview Lagos/Abuja Rent Prediction is a Machine Learning project developed as part of the 3MTT capstone project. The project addresses a common challenge faced by renters in Nigeria: the lack of reliable price benchmarks when searching for rental properties. The system uses property features such as location, number of bedrooms, bathrooms, toilets, serviced status, newly built status, and furnished status to predict house rent prices. ## 🎯 Problem Statement Renters in Lagos and Abuja often find it difficult to determine whether a property's asking price is reasonable because rental prices vary significantly depending on location and property features. The absence of accessible and reliable price benchmarks can make it difficult for renters to make informed housing decisions. This project aims to provide a data-driven approach for estimating rental prices based on available property characteristics. ## 💡 Project Objective The main objective of this project is to develop a Machine Learning model capable of predicting house rent prices in Lagos and Abuja based on property characteristics. The project also provides a simple Streamlit web application through which users can enter property details and receive an estimated rental price. ## 📊 Dataset The dataset contains 98,080 property listings and 10 columns of original featuers. The original dataset contains information about property titles, locations, prices, property features, and amenities. ### Main Features | Feature | Description | |---|---| | Title | Property listing title | | More Info | Additional property information | | Location | Property location | | Price | Property rental price | | Serviced | Indicates whether the property is serviced | | Newly Built | Indicates whether the property is newly built | | Furnished | Indicates whether the property …